Bibliographic record
Abstract
This paper examines empirically whether sophisticated speculative short sellers can detect earnings management by targeting stocks with large income-increasing discretionary accruals and high total accruals. Prior research indicates that total accruals are overpriced and this overpricing is largely attributable to the mispricing of discretionary accruals. Recent studies show that neither auditors nor financial analysts utilize information in accruals. Using samples of 11,537 firm-quarter observations and 5,118 firm-year observations for 1,146 12/31 non-financial NYSE firms from 1992 to 1999, I find supporting evidence those speculative short sellers can detect earnings management using financial accounting information disclosed in 10-Q and 10-K report. Specifically, I identify a significant and positive association between relative short interest and quarterly accruals. When I decompose accruals into its discretionary and non-discretionary components, I find that quarterly discretionary accruals are positively and significantly related to relative short interest. I further divide quarterly data into four sub-samples of separate fiscal quarters and find that speculative short sellers detect earnings management especially in the third and fourth quarters of a fiscal year and trade consistent with the information provided in quarterly accruals. In addition, the empirical results indicate that speculative short sellers establish short positions in firms with high accruals and large income-increasing discretionary accruals estimated using annual financial accounting information.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".